Analysing Political Biases in Danish Newspapers Using Sentiment Analysis

Kenneth C. Enevoldsen, Lasse Hansen


Traditionally, the evaluation of political biases in Danish newspapers has been carried out through
highly subjective methods. The conventional approach has been surveys asking samples of the
population to place various newspapers on the political spectrum, coupled with analysing voting
habits of the newspapers’ readers (Hjarvard, 2007). This paper seeks to examine whether it is
possible to use sentiment analysis to objectively assess political biases in Danish newspapers. By
using the sentiment dictionary AFINN (Nielsen et al., 2011), the mean sentiment scores for 360
articles was calculated. The articles were published in the Danish newspapers Berlingske and
Information and were all regarding the political parties Alternativet and Liberal Alliance. A
significant interaction effect between the parties and newspapers was discovered. This effect was
mainly driven by Information’s coverage of the two parties. Moreover, Berlingske was found to
publish a disproportionately greater number of articles concerning Liberal Alliance than
Alternativet. Based on these findings, an integration of sentiment analysis into the evaluation of
biases in news outlets is proposed. Furthermore, future studies are suggested to construct datasets
for evaluation of AFINN on news and to utilize web-mining methods to gather greater amounts of
data in order to analyse more parties and newspapers.


Sentiment Analysis; Opinion Mining; News Analysis; AFINN; Bag-of-Words

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